{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1103.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/1103.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uwog8RKZHRWu",
        "colab_type": "text"
      },
      "source": [
        "## 3.11 模型选择、欠拟合和过拟合"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWDCLv2eWC3H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install mxnet d2lzh"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tear-GJyYjAu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import autograd, gluon, nd \n",
        "from mxnet.gluon import data as gdata, loss as gloss, nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYSK8P7uY4wP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_train, n_test, true_w, true_b = 100, 100, [1.2, -3.4, 5.6], 5 \n",
        "features = nd.random.normal(shape=(n_train + n_test, 1))\n",
        "poly_features = nd.concat(features, nd.power(features, 2), nd.power(features, 3))\n",
        "labels = (true_w[0] * poly_features[:, 0] + true_w[1] * poly_features[:, 1] + true_w[2] * poly_features[:, 2] + true_b)\n",
        "labels += nd.random.normal(scale=0.1, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Q0rGkqiao-F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "c27b5006-db0b-496b-cb04-eb254c24ae4b"
      },
      "source": [
        "features[:2]"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 0.1816556 ]\n",
              " [-0.63245696]]\n",
              "<NDArray 2x1 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzzH69WWa5IV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "ceb91169-eb56-4661-bab5-8b6247737926"
      },
      "source": [
        "poly_features[:2]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 0.1816556   0.03299876  0.00599441]\n",
              " [-0.63245696  0.4000018  -0.25298393]]\n",
              "<NDArray 2x3 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqTBv9lWa9St",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "4c83e82b-337e-4fdc-bf77-826a9f807bed"
      },
      "source": [
        "labels[:2]"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[5.146566  1.4254713]\n",
              "<NDArray 2 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIXZqU_3a_lU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)):\n",
        "    d2l.set_figsize(figsize)\n",
        "    d2l.plt.xlabel(x_label)\n",
        "    d2l.plt.ylabel(y_label)\n",
        "    d2l.plt.semilogy(x_vals, y_vals)\n",
        "    if x2_vals and y2_vals:\n",
        "        d2l.plt.semilogy(x2_vals, y2_vals, linestyle=':')\n",
        "        d2l.plt.legend(legend)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jv9rNdjzcAPO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_epochs, loss = 100, gloss.L2Loss()\n",
        "\n",
        "def fit_and_plot(train_features, test_features, train_labels, test_labels):\n",
        "    net = nn.Sequential()\n",
        "    net.add(nn.Dense(1))\n",
        "    net.initialize()\n",
        "    batch_size = min(10, train_labels.shape[0])\n",
        "    train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
        "    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})\n",
        "    train_ls, test_ls = [], []\n",
        "    for _ in range(num_epochs):\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                l = loss(net(X), y)\n",
        "            l.backward()\n",
        "            trainer.step(batch_size)\n",
        "        train_ls.append(loss(net(train_features), train_labels).mean().asscalar())\n",
        "        test_ls.append(loss(net(test_features), test_labels).mean().asscalar())\n",
        "    print('final epoch: train loss', train_ls[-1], 'test loss', test_ls[-1])\n",
        "    semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss', range(1, num_epochs + 1), test_ls, ['train', 'test'])\n",
        "    print('weight:', net[0].weight.data().asnumpy(), '\\nbias:', net[0].bias.data().asnumpy())\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr1vOxFc1oNj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "3cd2f288-78b6-4cbf-f7da-eb8f72a6ab41"
      },
      "source": [
        "fit_and_plot(poly_features[:n_train, :], poly_features[n_train:, :], labels[:n_train], labels[n_train:])"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final epoch: train loss 0.0040140944 test loss 0.005357087\n",
            "weight: [[ 1.2035367 -3.3879864  5.595104 ]] \n",
            "bias: [4.977484]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
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    {
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      "outputs": [
        {
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            "final epoch: train loss 170.5034 test loss 183.81377\n",
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    {
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        "fit_and_plot(poly_features[0:2, :], poly_features[n_train:, :], labels[0:2], labels[n_train:])"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final epoch: train loss 2.3033557 test loss 391.29297\n",
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            "bias: [2.0942936]\n"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYb_6nIqJtIX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "from IPython import display \n",
        "from matplotlib import pyplot as plt \n",
        "from mxnet import autograd, nd \n",
        "import random "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtSyLTkf0dnu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs = 2 \n",
        "num_examples = 1000 \n",
        "true_w = [2, -3.4]\n",
        "true_b = 4.2 \n",
        "features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))\n",
        "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b \n",
        "labels += nd.random.normal(scale=0.01, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMHzM8qT2atP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "599149ba-066c-4a47-ae82-e8b59ab6fb58"
      },
      "source": [
        "features[0], labels[0]"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(\n",
              " [0.8208568  0.21493697]\n",
              " <NDArray 2 @cpu(0)>, \n",
              " [5.0867815]\n",
              " <NDArray 1 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fG9lRyU2gw9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "733947a0-bd71-4c25-dfb8-b6fb7897d628"
      },
      "source": [
        "def use_svg_display():\n",
        "    display.set_matplotlib_formats('svg')\n",
        "\n",
        "def set_figsize(figsize=(3.5, 2.5)):\n",
        "    use_svg_display()\n",
        "    plt.rcParams['figure.figsize'] = figsize \n",
        "\n",
        "set_figsize()\n",
        "plt.scatter(features[:, 1].asnumpy(), labels.asnumpy(), 1);"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xp10zuUv-7O_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2bae813e-a361-402d-c3c8-43886214eb17"
      },
      "source": [
        "# len(features)\n",
        "print(random.shuffle(list(range(10))))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97slDZAw3U1R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def data_iter(batch_size, features, labels):\n",
        "    num_examples = len(features)\n",
        "    indices = list(range(num_examples))\n",
        "    random.shuffle(indices)\n",
        "    for i in range(0, num_examples, batch_size):\n",
        "        j = nd.array(indices[i:min(i + batch_size, num_examples)])\n",
        "        yield features.take(j), labels.take(j)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gipr-_HIBBjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "c5197b1f-6ce1-4f35-9348-eeb5fdcceca2"
      },
      "source": [
        "batch_size = 10 \n",
        "\n",
        "for X, y in data_iter(batch_size, features, labels):\n",
        "    print(X, y)\n",
        "    break"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[-0.29187757  0.98311704]\n",
            " [-1.1017848  -1.1164639 ]\n",
            " [-0.27563417  1.5168767 ]\n",
            " [-2.3052864  -0.8243812 ]\n",
            " [ 1.4476801   0.74850816]\n",
            " [-0.9288544  -0.01012797]\n",
            " [ 0.5672225  -1.0133439 ]\n",
            " [ 0.674485   -0.5761929 ]\n",
            " [-2.008572    0.8848376 ]\n",
            " [ 0.47415277 -0.8507774 ]]\n",
            "<NDArray 10x2 @cpu(0)> \n",
            "[ 0.26157156  5.804421   -1.5193472   2.3983386   4.539287    2.3718019\n",
            "  8.776412    7.512675   -2.8187766   8.036687  ]\n",
            "<NDArray 10 @cpu(0)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaC-F-jPBQUB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w = nd.random.normal(scale=0.01, shape=(num_inputs, 1))\n",
        "b = nd.zeros(shape=(1,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PINeNU1nBpgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "552697a7-7256-42b7-c696-bf74d6d0e85f"
      },
      "source": [
        "w.attach_grad\n",
        "b.attach_grad "
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDArray.attach_grad of \n",
              "[0.]\n",
              "<NDArray 1 @cpu(0)>>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_ZxVGnxBtFr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linreg(X, w, b):\n",
        "    return nd.dot(X, w) + b "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qiiag00pB3Ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def squared_loss(y_hat, y):\n",
        "    return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4oC8WsJCyeP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sgd(params, lr, batch_size):\n",
        "    for param in params:\n",
        "        param[:] = param - lr * param.grad / batch_size "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9Yf1mr6DIcK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr = 0.03 \n",
        "num_epochs = 3 \n",
        "net = linreg \n",
        "loss = squared_loss \n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for X, y in data_iter(batch_size, features, labels):\n",
        "        with autograd.record():\n",
        "            l = loss(net(X, w, b), y)\n",
        "        l.backward()\n",
        "        sgd([w, b], lr, batch_size)\n",
        "    train_l = loss(net(features, w, b), labels)\n",
        "    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().asnumpy()))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbAWsW-9FiQY",
        "colab_type": "text"
      },
      "source": [
        "## 3.3 线性回归的简洁实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s81KJPi9Hrj9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import autograd, nd \n",
        "\n",
        "num_inputs = 2 \n",
        "num_examples = 1000 \n",
        "true_w = [2, -3.4]\n",
        "true_b = 4.2 \n",
        "features = nd.random.normal(scale=1, shape=(num_examples, num_inputs))\n",
        "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b\n",
        "labels += nd.random.normal(scale=0.01, shape=labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8nCp_XQITyK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import data as gdata \n",
        "\n",
        "batch_size = 10 \n",
        "dataset = gdata.ArrayDataset(features, labels)\n",
        "data_iter = gdata.DataLoader(dataset, batch_size, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3gYa_hRI-n8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "20d0b755-75b5-49e1-9149-6395858d4e77"
      },
      "source": [
        "for X, y in data_iter:\n",
        "    print(X, y)\n",
        "    break"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[-0.23539764  0.6447937 ]\n",
            " [-0.2078483   0.90913844]\n",
            " [ 0.79349494  0.58542407]\n",
            " [ 2.7504392   1.4042664 ]\n",
            " [-0.10717125  0.79039854]\n",
            " [ 2.1810858   1.8891623 ]\n",
            " [-0.29291564  0.5742522 ]\n",
            " [ 0.7726559   0.47493884]\n",
            " [-0.9688528   0.1770301 ]\n",
            " [ 1.1270262  -1.1154262 ]]\n",
            "<NDArray 10x2 @cpu(0)> \n",
            "[ 1.5315362  0.6750592  3.7886255  4.920602   1.3204547  2.1565669\n",
            "  1.6625717  4.131741   1.6629494 10.237064 ]\n",
            "<NDArray 10 @cpu(0)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiwWTYtvJDqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import nn \n",
        "\n",
        "net = nn.Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-qbeOeFJd3T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net.add(nn.Dense(1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBIKUbIFJn8q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import init \n",
        "\n",
        "net.initialize(init.Normal(sigma=0.01))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6m0jPHjoJ1SJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet.gluon import loss as gloss \n",
        "\n",
        "loss = gloss.L2Loss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1-by9rQJ8_b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mxnet import gluon \n",
        "\n",
        "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16ji18cgKXkd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "6eb6e3ad-bf28-4854-ad17-93801af38285"
      },
      "source": [
        "num_epochs = 3 \n",
        "for epoch in range(1, num_epochs + 1):\n",
        "    for X, y in data_iter:\n",
        "        with autograd.record():\n",
        "            l = loss(net(X), y)\n",
        "        l.backward()\n",
        "        trainer.step(batch_size)\n",
        "    l = loss(net(features), labels)\n",
        "    print('epoch %d, loss: %f' % (epoch, l.mean().asnumpy()))"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss: 0.028785\n",
            "epoch 2, loss: 0.000099\n",
            "epoch 3, loss: 0.000048\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSmLcWPVLHEL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "bba1495c-2612-4b4f-d336-8a13622264de"
      },
      "source": [
        "dense = net[0]\n",
        "true_w, dense.weight.data()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([2, -3.4], \n",
              " [[ 1.9996848 -3.400023 ]]\n",
              " <NDArray 1x2 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS-xhRjfL3tG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a83498ce-58a6-49bb-e49f-b7acf4f2b083"
      },
      "source": [
        "true_b, dense.bias.data()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4.2, \n",
              " [4.199382]\n",
              " <NDArray 1 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCkl-eYY7oe1",
        "colab_type": "text"
      },
      "source": [
        "## 3.12 权重衰减"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2viNiXd7_Zl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import autograd, gluon, init, nd \n",
        "from mxnet.gluon import data as gdata, loss as gloss, nn \n",
        "\n",
        "n_train, n_test, num_inputs = 20, 100, 200 \n",
        "true_w, true = nd.ones((num_inputs, 1)) * 0.01, 0.05 \n",
        "\n",
        "features = nd.random.normal(shape=(n_train + n_test, num_inputs))\n",
        "labels = nd.dot(features, true_w) + true_b \n",
        "labels += nd.random.normal(scale=0.01, shape=labels.shape)\n",
        "train_features, test_features = features[:n_train, :], features[n_train:, :]\n",
        "train_labels, test_labels = labels[:n_train], labels[n_train:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyszDmnDeq2r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def init_params():\n",
        "    w = nd.random.normal(scale=1, shape=(num_inputs, 1))\n",
        "    b = nd.zeros(shape=(1, ))\n",
        "    w.attach_grad()\n",
        "    b.attach_grad()\n",
        "    return [w, b]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3b6jJnb6f2Ii",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def l2_penalty(w):\n",
        "    return (w**2).sum() / 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DawUcB1uf8gk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size, num_epochs, lr = 1, 100, 0.003\n",
        "net, loss = d2l.linreg, d2l.squared_loss\n",
        "train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
        "\n",
        "def fit_and_plot(lambd):\n",
        "    w, b = init_params()\n",
        "    train_ls, test_ls = [], []\n",
        "    for _ in range(num_epochs):\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                l = loss(net(X, w, b), y) + lambd * l2_penalty(w)\n",
        "            l.backward()\n",
        "            d2l.sgd([w, b], lr, batch_size)\n",
        "        train_ls.append(loss(net(train_features, w, b), train_labels).mean().asscalar())\n",
        "        test_ls.append(loss(net(test_features, w, b), test_labels).mean().asscalar())\n",
        "    \n",
        "    d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', loss, \n",
        "            range(1, num_epochs + 1), test_ls, ['train', 'test'])\n",
        "    print('L2 norm of w:', w.norm().asscalar())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpUxMoQWkNHr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "9ead7ac6-cf91-41c5-ab13-7d76f84b036a"
      },
      "source": [
        "fit_and_plot(lambd=0)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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        "fit_and_plot(lambd=3)"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "L2 norm of w: 0.28940323\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjx47EL4lCNu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit_and_plot_gluon(wd):\n",
        "    net = nn.Sequential()\n",
        "    net.add(nn.Dense(1))\n",
        "    net.initialize(init.Normal(sigma=1))\n",
        "    trainer_w = gluon.Trainer(net.collect_params('.*weight'), 'sgd', {'learning_rate': lr, 'wd': wd})\n",
        "    trainer_b = gluon.Trainer(net.collect_params('.*bias'), 'sgd', {'learning_rate': lr})\n",
        "    train_ls, test_ls = [], []\n",
        "    for _ in range(num_epochs):\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                l = loss(net(X), y)\n",
        "            l.backward()\n",
        "            trainer_w.step(batch_size)\n",
        "            trainer_b.step(batch_size)\n",
        "        train_ls.append(loss(net(train_features), train_labels).mean().asscalar())\n",
        "        test_ls.append(loss(net(test_features), test_labels).mean().asscalar())\n",
        "    d2l.semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'loss', range(1, num_epochs+1), test_ls, ['train', 'test'])\n",
        "    print('L2 norma of w:', net[0].weight.data().norm().asscalar())\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKfVo57mnxm9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "0b2bf36d-349e-4471-b4ae-b5fc42ce8211"
      },
      "source": [
        "fit_and_plot_gluon(0)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "L2 norma of w: 12.639709\n"
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    {
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      "source": [
        "fit_and_plot_gluon(3)"
      ],
      "execution_count": 134,
      "outputs": [
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "L2 norma of w: 0.27185556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IM8e-182oRTQ",
        "colab_type": "text"
      },
      "source": [
        "## 3.13 丢弃法"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYY3c_wqofiP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import d2lzh as d2l\n",
        "from mxnet import autograd, gluon, init, nd \n",
        "from mxnet.gluon import loss as gloss, nn \n",
        "\n",
        "def dropout(X, drop_prob):\n",
        "    assert 0 <= drop_prob <= 1 \n",
        "    keep_prob = 1 - drop_prob \n",
        "    if keep_prob == 0:\n",
        "        return X.zeros_like()\n",
        "    mask = nd.random.uniform(0, 1, X.shape) < keep_prob\n",
        "    return mask * X / keep_prob "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtUcXMHPmrnb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "a3164028-c652-4c8c-e4ac-ec54b3339183"
      },
      "source": [
        "X = nd.arange(16).reshape((2, 8))\n",
        "dropout(X, 0)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 0.  1.  2.  3.  4.  5.  6.  7.]\n",
              " [ 8.  9. 10. 11. 12. 13. 14. 15.]]\n",
              "<NDArray 2x8 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "122xAhYtnp9r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "890b82e8-3000-4559-93de-005900507128"
      },
      "source": [
        "dropout(X, 0.5)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 0.  2.  4.  6.  8. 10.  0. 14.]\n",
              " [16.  0.  0. 22.  0. 26.  0.  0.]]\n",
              "<NDArray 2x8 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKWCh4WqnuUf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "18291f9f-d145-4f78-f648-74c68b0c9fb8"
      },
      "source": [
        "dropout(X, 0.8)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 0.  0.  0.  0.  0.  0.  0. 35.]\n",
              " [ 0.  0.  0.  0.  0. 65.  0.  0.]]\n",
              "<NDArray 2x8 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7MeWxg7n88-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "48a9c32a-c1ca-4dcb-c69a-f66787bc7a65"
      },
      "source": [
        "dropout(X, 1)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[0. 0. 0. 0. 0. 0. 0. 0.]\n",
              " [0. 0. 0. 0. 0. 0. 0. 0.]]\n",
              "<NDArray 2x8 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LL5UZGPgoAD2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256 \n",
        "\n",
        "W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens1))\n",
        "b1 = nd.zeros(num_hiddens1)\n",
        "W2 = nd.random.normal(scale=0.01, shape=(num_hiddens1, num_hiddens2))\n",
        "b2 = nd.zeros(num_hiddens2)\n",
        "W3 = nd.random.normal(scale=0.01, shape=(num_hiddens2, num_outputs))\n",
        "b3 = nd.zeros(num_outputs)\n",
        "\n",
        "params = [W1, b1, W2, b2, W3, b3]\n",
        "for param in params:\n",
        "    param.attach_grad()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuGb_snVrJ4M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drop_prob1, drop_prob2 = 0.2, 0.5 \n",
        "\n",
        "def net(X):\n",
        "    X = X.reshape((-1, num_inputs))\n",
        "    H1 = (nd.dot(X, W1) + b1).relu()\n",
        "    if autograd.is_training():\n",
        "        H1 = dropout(H1, drop_prob1)\n",
        "    H2 = (nd.dot(H1, W2) + b2).relu()\n",
        "    if autograd.is_training():\n",
        "        H2 = dropout(H2, drop_prob1)\n",
        "    return nd.dot(H2, W3) + b3 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ss_aJa9vl5d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "c50c60ce-1152-4e00-ae73-b325e972ddd6"
      },
      "source": [
        "num_epochs, lr, batch_size = 5, 0.5, 256 \n",
        "\n",
        "loss = gloss.SoftmaxCrossEntropyLoss()\n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
        "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 1.1582, train acc 0.552, test acc 0.783\n",
            "epoch 2, loss 0.5649, train acc 0.791, test acc 0.830\n",
            "epoch 3, loss 0.4748, train acc 0.826, test acc 0.852\n",
            "epoch 4, loss 0.4336, train acc 0.841, test acc 0.863\n",
            "epoch 5, loss 0.4054, train acc 0.851, test acc 0.859\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rR--Ifv6wCUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "4af79d12-aa8a-4926-e313-f2cf57dbe845"
      },
      "source": [
        "for X, y in test_iter:\n",
        "    break\n",
        "\n",
        "true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())\n",
        "pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())\n",
        "titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",
        "\n",
        "d2l.show_fashion_mnist(X[0:9], titles[0:9])"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85-wqjP1wwdp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = nn.Sequential()\n",
        "net.add(nn.Dense(256, activation='relu'), \n",
        "    nn.Dropout(drop_prob1), \n",
        "    nn.Dense(256, activation='relu'), \n",
        "    nn.Dropout(drop_prob2), \n",
        "    nn.Dense(10))\n",
        "net.initialize(init.Normal(sigma=0.01))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xrg27hkpDoHE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "a1302ca7-ef95-4acf-c491-77ffd03a4ce5"
      },
      "source": [
        "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})\n",
        "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, trainer)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 1.1147, train acc 0.570, test acc 0.790\n",
            "epoch 2, loss 0.5781, train acc 0.788, test acc 0.842\n",
            "epoch 3, loss 0.4825, train acc 0.825, test acc 0.856\n",
            "epoch 4, loss 0.4466, train acc 0.838, test acc 0.849\n",
            "epoch 5, loss 0.4154, train acc 0.847, test acc 0.867\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d53324c1-8d52-47dc-efee-1b6095861bc0",
        "id": "5VIb1LuEP4zH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "for X, y in test_iter:\n",
        "    break\n",
        "\n",
        "true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())\n",
        "pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())\n",
        "titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",
        "\n",
        "d2l.show_fashion_mnist(X[0:9], titles[0:9])"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNiEaoa-EHAW",
        "colab_type": "text"
      },
      "source": [
        "## 3.16 实战 kaggle 比赛：房价预测"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyNn7RPBUKIQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files \n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7yEEBZ7U6lP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l \n",
        "from mxnet import autograd, gluon, init, nd \n",
        "from mxnet.gluon import data as gdata, loss as gloss, nn \n",
        "import numpy as np \n",
        "import pandas as pd "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7hTyCVXV3DC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = pd.read_csv('train.csv')\n",
        "test_data = pd.read_csv('test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyg3aekMWH16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "babfeb03-d658-4335-e849-46dc2162b0a8"
      },
      "source": [
        "train_data.shape "
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbSyQmV0WLLy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf165dde-ca82-45c0-fbc0-7aea50fa92dd"
      },
      "source": [
        "test_data.shape "
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1459, 80)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_oHugJxWRQs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "outputId": "55c643ed-1fda-46a5-8e49-988dd1cd7631"
      },
      "source": [
        "train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage SaleType SaleCondition  SalePrice\n",
              "0   1          60       RL         65.0       WD        Normal     208500\n",
              "1   2          20       RL         80.0       WD        Normal     181500\n",
              "2   3          60       RL         68.0       WD        Normal     223500\n",
              "3   4          70       RL         60.0       WD       Abnorml     140000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiZmdwTrWnIa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-iTGinNXDgj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index \n",
        "all_features[numeric_features] = all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std()))\n",
        "all_features[numeric_features] = all_features[numeric_features].fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FNqzWdEtrN6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad647834-a734-4ec2-cd23-ae7bc0f2a1c6"
      },
      "source": [
        "all_features = pd.get_dummies(all_features, dummy_na=True)\n",
        "all_features.shape"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2919, 331)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6jClfZzwKQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_train = train_data.shape[0]\n",
        "train_features = nd.array(all_features[:n_train].values)\n",
        "test_features = nd.array(all_features[n_train:].values)\n",
        "train_labels = nd.array(train_data.SalePrice.values).reshape((-1, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UK4xAAT6512y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = gloss.L2Loss()\n",
        "\n",
        "def get_net():\n",
        "    net = nn.Sequential()\n",
        "    net.add(nn.Dense(1))\n",
        "    net.initialize()\n",
        "    return net "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb5TmGbk6QNV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def log_rmse(net, features, labels):\n",
        "    clipped_preds = nd.clip(net(features), 1, float('inf'))\n",
        "    rmse = nd.sqrt(2 * loss(clipped_preds.log(), labels.log()).mean())\n",
        "    return rmse.asscalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccVK8Mjz631P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):\n",
        "    train_ls, test_ls = [], []\n",
        "    train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
        "    trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': learning_rate, 'wd': weight_decay})\n",
        "    for epoch in range(num_epochs):\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                l = loss(net(X), y)\n",
        "            l.backward()\n",
        "            trainer.step(batch_size)\n",
        "        train_ls.append(log_rmse(net, train_features, train_labels))\n",
        "        if test_labels is not None:\n",
        "            test_ls.append(log_rmse(net, test_features, test_labels))\n",
        "    return train_ls, test_ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JG7-8AY5-31j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_k_fold_data(k, i, X, y):\n",
        "    assert k > 1  \n",
        "    fold_size = X.shape[0] // k \n",
        "    X_train, y_train = None, None \n",
        "    for j in range(k):\n",
        "        idx = slice(j * fold_size, (j + 1) * fold_size)\n",
        "        X_part, y_part = X[idx, :], y[idx]\n",
        "        if j == i:\n",
        "            X_valid, y_valid = X_part, y_part\n",
        "        elif X_train is None:\n",
        "            X_train, y_train = X_part, y_part \n",
        "        else:\n",
        "            X_train = nd.concat(X_train, X_part, dim=0)\n",
        "            y_train = nd.concat(y_train, y_part, dim=0)\n",
        "    return X_train, y_train, X_valid, y_valid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWtalkJFANcr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):\n",
        "    train_l_sum, valid_l_sum = 0, 0 \n",
        "    for i in range(k):\n",
        "        data = get_k_fold_data(k, i, X_train, y_train)\n",
        "        net = get_net()\n",
        "        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate, weight_decay, batch_size)\n",
        "        train_l_sum += train_ls[-1]\n",
        "        valid_l_sum += valid_ls[-1]\n",
        "        if i == 0:\n",
        "            d2l.semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'rmse', range(1, num_epochs+1), valid_ls, ['train', 'valid'])\n",
        "        print('fold %d, train rmse %f, valid rmse %f' % (i, train_ls[-1], valid_ls[-1]))\n",
        "    return train_l_sum / k, valid_l_sum / k"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59f7P3UGCPc8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "943fe034-7931-42d0-8129-a4ca25e47fd3"
      },
      "source": [
        "k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64 \n",
        "train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr, weight_decay, batch_size)\n",
        "print('%d-fold validation: avg train rmse %f, avg valid rmse %f' % (k, train_l, valid_l))"
      ],
      "execution_count": 163,
      "outputs": [
        {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "fold 0, train rmse 0.169755, valid rmse 0.157051\n",
            "fold 1, train rmse 0.162174, valid rmse 0.188838\n",
            "fold 2, train rmse 0.163699, valid rmse 0.168018\n",
            "fold 3, train rmse 0.168150, valid rmse 0.154837\n",
            "fold 4, train rmse 0.162961, valid rmse 0.182832\n",
            "5-fold validation: avg train rmse 0.165348, avg valid rmse 0.170315\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3nDST1kD3cg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):\n",
        "    net = get_net()\n",
        "    train_ls, _ = train(net, train_features, train_labels, None, None, num_epochs, lr, weight_decay, batch_size)\n",
        "    d2l.semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'rmse')\n",
        "    print('train rmse %f' % train_ls[-1])\n",
        "    preds = net(test_features).asnumpy()\n",
        "    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
        "    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)\n",
        "    submission.to_csv('submission.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QvUc37TF3Io",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "65e3c92d-a172-49a1-e0ba-b0f3ea28b8cb"
      },
      "source": [
        "train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "display_data",
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